Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations5960
Missing cells5271
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory211.6 B

Variable types

Categorical3
Numeric10

Alerts

MORTDUE is highly overall correlated with VALUEHigh correlation
VALUE is highly overall correlated with MORTDUEHigh correlation
MORTDUE has 518 (8.7%) missing values Missing
VALUE has 112 (1.9%) missing values Missing
REASON has 252 (4.2%) missing values Missing
JOB has 279 (4.7%) missing values Missing
YOJ has 515 (8.6%) missing values Missing
DEROG has 708 (11.9%) missing values Missing
DELINQ has 580 (9.7%) missing values Missing
CLAGE has 308 (5.2%) missing values Missing
NINQ has 510 (8.6%) missing values Missing
CLNO has 222 (3.7%) missing values Missing
DEBTINC has 1267 (21.3%) missing values Missing
YOJ has 415 (7.0%) zeros Zeros
DEROG has 4527 (76.0%) zeros Zeros
DELINQ has 4179 (70.1%) zeros Zeros
NINQ has 2531 (42.5%) zeros Zeros
CLNO has 62 (1.0%) zeros Zeros

Reproduction

Analysis started2025-04-08 19:48:17.963885
Analysis finished2025-04-08 19:48:21.979512
Duration4.02 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

BAD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
4771 
1
1189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Length

2025-04-08T15:48:22.115376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-08T15:48:22.140103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4771
80.1%
1 1189
 
19.9%

LOAN
Real number (ℝ)

Distinct540
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18607.97
Minimum1100
Maximum89900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.171386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile5900
Q111100
median16300
Q323300
95-th percentile40000
Maximum89900
Range88800
Interquartile range (IQR)12200

Descriptive statistics

Standard deviation11207.48
Coefficient of variation (CV)0.60229464
Kurtosis6.9325898
Mean18607.97
Median Absolute Deviation (MAD)6000
Skewness2.0237807
Sum1.109035 × 108
Variance1.2560762 × 108
MonotonicityIncreasing
2025-04-08T15:48:22.211291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 105
 
1.8%
10000 81
 
1.4%
20000 74
 
1.2%
25000 73
 
1.2%
12000 69
 
1.2%
17000 51
 
0.9%
13000 50
 
0.8%
5000 50
 
0.8%
11000 47
 
0.8%
8000 44
 
0.7%
Other values (530) 5316
89.2%
ValueCountFrequency (%)
1100 1
 
< 0.1%
1300 1
 
< 0.1%
1500 2
 
< 0.1%
1700 2
 
< 0.1%
1800 2
 
< 0.1%
2000 6
0.1%
2100 1
 
< 0.1%
2200 3
0.1%
2300 3
0.1%
2400 6
0.1%
ValueCountFrequency (%)
89900 1
< 0.1%
89800 1
< 0.1%
89200 1
< 0.1%
89000 1
< 0.1%
88900 2
< 0.1%
88800 1
< 0.1%
88500 1
< 0.1%
88300 1
< 0.1%
87500 1
< 0.1%
87000 1
< 0.1%

MORTDUE
Real number (ℝ)

High correlation  Missing 

Distinct5053
Distinct (%)92.9%
Missing518
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean73760.817
Minimum2063
Maximum399550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.252339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2063
5-th percentile18232.4
Q146276
median65019
Q391488
95-th percentile151999.55
Maximum399550
Range397487
Interquartile range (IQR)45212

Descriptive statistics

Standard deviation44457.609
Coefficient of variation (CV)0.60272664
Kurtosis6.4818663
Mean73760.817
Median Absolute Deviation (MAD)21655.5
Skewness1.8144807
Sum4.0140637 × 108
Variance1.976479 × 109
MonotonicityNot monotonic
2025-04-08T15:48:22.296508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42000 11
 
0.2%
47000 10
 
0.2%
65000 9
 
0.2%
124000 7
 
0.1%
45000 7
 
0.1%
62000 7
 
0.1%
70000 7
 
0.1%
55000 7
 
0.1%
50000 7
 
0.1%
58000 6
 
0.1%
Other values (5043) 5364
90.0%
(Missing) 518
 
8.7%
ValueCountFrequency (%)
2063 1
< 0.1%
2619 1
< 0.1%
2800 1
< 0.1%
3372 1
< 0.1%
4000 1
< 0.1%
4447 1
< 0.1%
4500 1
< 0.1%
4641 1
< 0.1%
4734 1
< 0.1%
4742 1
< 0.1%
ValueCountFrequency (%)
399550 1
< 0.1%
399412 1
< 0.1%
397299 1
< 0.1%
391000 1
< 0.1%
371003 1
< 0.1%
369874 1
< 0.1%
367917 1
< 0.1%
367089 1
< 0.1%
365528 1
< 0.1%
363737 1
< 0.1%

VALUE
Real number (ℝ)

High correlation  Missing 

Distinct5381
Distinct (%)92.0%
Missing112
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean101776.05
Minimum8000
Maximum855909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.339204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile39050.7
Q166075.5
median89235.5
Q3119824.25
95-th percentile203717.2
Maximum855909
Range847909
Interquartile range (IQR)53748.75

Descriptive statistics

Standard deviation57385.775
Coefficient of variation (CV)0.56384362
Kurtosis24.362805
Mean101776.05
Median Absolute Deviation (MAD)25764.5
Skewness3.0533443
Sum5.9518633 × 108
Variance3.2931272 × 109
MonotonicityNot monotonic
2025-04-08T15:48:22.379120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 15
 
0.3%
80000 14
 
0.2%
85000 12
 
0.2%
65000 11
 
0.2%
78000 10
 
0.2%
72000 9
 
0.2%
68000 8
 
0.1%
87000 8
 
0.1%
50000 8
 
0.1%
125000 7
 
0.1%
Other values (5371) 5746
96.4%
(Missing) 112
 
1.9%
ValueCountFrequency (%)
8000 1
< 0.1%
8800 1
< 0.1%
9100 1
< 0.1%
9500 1
< 0.1%
11550 1
< 0.1%
11702 1
< 0.1%
12414 1
< 0.1%
12500 1
< 0.1%
12737 1
< 0.1%
12972 1
< 0.1%
ValueCountFrequency (%)
855909 1
< 0.1%
854114 1
< 0.1%
854112 1
< 0.1%
850000 1
< 0.1%
512650 1
< 0.1%
511164 1
< 0.1%
505000 1
< 0.1%
471827 1
< 0.1%
469771 1
< 0.1%
469748 1
< 0.1%

REASON
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing252
Missing (%)4.2%
Memory size372.6 KiB
DebtCon
3928 
HomeImp
1780 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters39956
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHomeImp
2nd rowHomeImp
3rd rowHomeImp
4th rowHomeImp
5th rowHomeImp

Common Values

ValueCountFrequency (%)
DebtCon 3928
65.9%
HomeImp 1780
29.9%
(Missing) 252
 
4.2%

Length

2025-04-08T15:48:22.413475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-08T15:48:22.537090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
debtcon 3928
68.8%
homeimp 1780
31.2%

Most occurring characters

ValueCountFrequency (%)
e 5708
14.3%
o 5708
14.3%
D 3928
9.8%
b 3928
9.8%
t 3928
9.8%
C 3928
9.8%
n 3928
9.8%
m 3560
8.9%
H 1780
 
4.5%
I 1780
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5708
14.3%
o 5708
14.3%
D 3928
9.8%
b 3928
9.8%
t 3928
9.8%
C 3928
9.8%
n 3928
9.8%
m 3560
8.9%
H 1780
 
4.5%
I 1780
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5708
14.3%
o 5708
14.3%
D 3928
9.8%
b 3928
9.8%
t 3928
9.8%
C 3928
9.8%
n 3928
9.8%
m 3560
8.9%
H 1780
 
4.5%
I 1780
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5708
14.3%
o 5708
14.3%
D 3928
9.8%
b 3928
9.8%
t 3928
9.8%
C 3928
9.8%
n 3928
9.8%
m 3560
8.9%
H 1780
 
4.5%
I 1780
 
4.5%

JOB
Categorical

Missing 

Distinct6
Distinct (%)0.1%
Missing279
Missing (%)4.7%
Memory size363.3 KiB
Other
2388 
ProfExe
1276 
Office
948 
Mgr
767 
Self
 
193

Length

Max length7
Median length6
Mean length5.3120929
Min length3

Characters and Unicode

Total characters30178
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowOffice
5th rowOther

Common Values

ValueCountFrequency (%)
Other 2388
40.1%
ProfExe 1276
21.4%
Office 948
 
15.9%
Mgr 767
 
12.9%
Self 193
 
3.2%
Sales 109
 
1.8%
(Missing) 279
 
4.7%

Length

2025-04-08T15:48:22.566018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-08T15:48:22.593447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
other 2388
42.0%
profexe 1276
22.5%
office 948
 
16.7%
mgr 767
 
13.5%
self 193
 
3.4%
sales 109
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 4914
16.3%
r 4431
14.7%
f 3365
11.2%
O 3336
11.1%
h 2388
7.9%
t 2388
7.9%
E 1276
 
4.2%
x 1276
 
4.2%
o 1276
 
4.2%
P 1276
 
4.2%
Other values (8) 4252
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4914
16.3%
r 4431
14.7%
f 3365
11.2%
O 3336
11.1%
h 2388
7.9%
t 2388
7.9%
E 1276
 
4.2%
x 1276
 
4.2%
o 1276
 
4.2%
P 1276
 
4.2%
Other values (8) 4252
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4914
16.3%
r 4431
14.7%
f 3365
11.2%
O 3336
11.1%
h 2388
7.9%
t 2388
7.9%
E 1276
 
4.2%
x 1276
 
4.2%
o 1276
 
4.2%
P 1276
 
4.2%
Other values (8) 4252
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4914
16.3%
r 4431
14.7%
f 3365
11.2%
O 3336
11.1%
h 2388
7.9%
t 2388
7.9%
E 1276
 
4.2%
x 1276
 
4.2%
o 1276
 
4.2%
P 1276
 
4.2%
Other values (8) 4252
14.1%

YOJ
Real number (ℝ)

Missing  Zeros 

Distinct99
Distinct (%)1.8%
Missing515
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.9222681
Minimum0
Maximum41
Zeros415
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.631533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile24
Maximum41
Range41
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.5739822
Coefficient of variation (CV)0.8488853
Kurtosis0.37207248
Mean8.9222681
Median Absolute Deviation (MAD)5
Skewness0.98846007
Sum48581.75
Variance57.365207
MonotonicityNot monotonic
2025-04-08T15:48:22.671150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 415
 
7.0%
1 363
 
6.1%
2 347
 
5.8%
5 333
 
5.6%
4 324
 
5.4%
6 318
 
5.3%
3 307
 
5.2%
9 286
 
4.8%
10 285
 
4.8%
8 256
 
4.3%
Other values (89) 2211
37.1%
(Missing) 515
 
8.6%
ValueCountFrequency (%)
0 415
7.0%
0.1 14
 
0.2%
0.2 10
 
0.2%
0.25 1
 
< 0.1%
0.3 7
 
0.1%
0.4 9
 
0.2%
0.5 7
 
0.1%
0.6 4
 
0.1%
0.7 4
 
0.1%
0.75 2
 
< 0.1%
ValueCountFrequency (%)
41 3
 
0.1%
36 5
 
0.1%
35 5
 
0.1%
34 2
 
< 0.1%
33 2
 
< 0.1%
32 1
 
< 0.1%
31 12
 
0.2%
30 30
0.5%
29.9 1
 
< 0.1%
29 29
0.5%

DEROG
Real number (ℝ)

Missing  Zeros 

Distinct11
Distinct (%)0.2%
Missing708
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean0.25456969
Minimum0
Maximum10
Zeros4527
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.701776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84604678
Coefficient of variation (CV)3.3234388
Kurtosis36.872763
Mean0.25456969
Median Absolute Deviation (MAD)0
Skewness5.3208703
Sum1337
Variance0.71579515
MonotonicityNot monotonic
2025-04-08T15:48:22.728103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4527
76.0%
1 435
 
7.3%
2 160
 
2.7%
3 58
 
1.0%
4 23
 
0.4%
5 15
 
0.3%
6 15
 
0.3%
7 8
 
0.1%
8 6
 
0.1%
9 3
 
0.1%
(Missing) 708
 
11.9%
ValueCountFrequency (%)
0 4527
76.0%
1 435
 
7.3%
2 160
 
2.7%
3 58
 
1.0%
4 23
 
0.4%
5 15
 
0.3%
6 15
 
0.3%
7 8
 
0.1%
8 6
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 3
 
0.1%
8 6
 
0.1%
7 8
 
0.1%
6 15
 
0.3%
5 15
 
0.3%
4 23
 
0.4%
3 58
 
1.0%
2 160
 
2.7%
1 435
7.3%

DELINQ
Real number (ℝ)

Missing  Zeros 

Distinct14
Distinct (%)0.3%
Missing580
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean0.44944238
Minimum0
Maximum15
Zeros4179
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.752827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1272659
Coefficient of variation (CV)2.5081434
Kurtosis23.565449
Mean0.44944238
Median Absolute Deviation (MAD)0
Skewness4.0231496
Sum2418
Variance1.2707284
MonotonicityNot monotonic
2025-04-08T15:48:22.781082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 4179
70.1%
1 654
 
11.0%
2 250
 
4.2%
3 129
 
2.2%
4 78
 
1.3%
5 38
 
0.6%
6 27
 
0.5%
7 13
 
0.2%
8 5
 
0.1%
10 2
 
< 0.1%
Other values (4) 5
 
0.1%
(Missing) 580
 
9.7%
ValueCountFrequency (%)
0 4179
70.1%
1 654
 
11.0%
2 250
 
4.2%
3 129
 
2.2%
4 78
 
1.3%
5 38
 
0.6%
6 27
 
0.5%
7 13
 
0.2%
8 5
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
8 5
 
0.1%
7 13
 
0.2%
6 27
 
0.5%
5 38
0.6%
4 78
1.3%

CLAGE
Real number (ℝ)

Missing 

Distinct5314
Distinct (%)94.0%
Missing308
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean179.76628
Minimum0
Maximum1168.2336
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.816432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.912654
Q1115.1167
median173.46667
Q3231.56228
95-th percentile321.63333
Maximum1168.2336
Range1168.2336
Interquartile range (IQR)116.44558

Descriptive statistics

Standard deviation85.810092
Coefficient of variation (CV)0.47734255
Kurtosis7.5995493
Mean179.76628
Median Absolute Deviation (MAD)58.270411
Skewness1.343412
Sum1016039
Variance7363.3718
MonotonicityNot monotonic
2025-04-08T15:48:22.858687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.5 7
 
0.1%
206.9666667 7
 
0.1%
177.5 6
 
0.1%
123.7666667 6
 
0.1%
95.36666667 6
 
0.1%
109.5666667 6
 
0.1%
304.3666667 5
 
0.1%
97.4 5
 
0.1%
118.6666667 5
 
0.1%
232.3333333 5
 
0.1%
Other values (5304) 5594
93.9%
(Missing) 308
 
5.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.4867114508 1
< 0.1%
0.5071145295 1
< 0.1%
2.033333333 1
< 0.1%
2.820785578 1
< 0.1%
3.04438414 1
< 0.1%
4.412770061 1
< 0.1%
5.243341044 1
< 0.1%
6.133333333 1
< 0.1%
8.055265077 1
< 0.1%
ValueCountFrequency (%)
1168.233561 1
< 0.1%
1154.633333 1
< 0.1%
649.7471044 1
< 0.1%
648.3284926 1
< 0.1%
639.0581723 1
< 0.1%
638.2753611 1
< 0.1%
634.4618926 1
< 0.1%
632.1031857 1
< 0.1%
630.0333333 1
< 0.1%
629.0957663 1
< 0.1%

NINQ
Real number (ℝ)

Missing  Zeros 

Distinct16
Distinct (%)0.3%
Missing510
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean1.186055
Minimum0
Maximum17
Zeros2531
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.891470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.728675
Coefficient of variation (CV)1.4574998
Kurtosis9.7865073
Mean1.186055
Median Absolute Deviation (MAD)1
Skewness2.6219842
Sum6464
Variance2.9883172
MonotonicityNot monotonic
2025-04-08T15:48:22.920772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 2531
42.5%
1 1339
22.5%
2 780
 
13.1%
3 392
 
6.6%
4 156
 
2.6%
5 75
 
1.3%
6 56
 
0.9%
7 44
 
0.7%
10 28
 
0.5%
8 22
 
0.4%
Other values (6) 27
 
0.5%
(Missing) 510
 
8.6%
ValueCountFrequency (%)
0 2531
42.5%
1 1339
22.5%
2 780
 
13.1%
3 392
 
6.6%
4 156
 
2.6%
5 75
 
1.3%
6 56
 
0.9%
7 44
 
0.7%
8 22
 
0.4%
9 11
 
0.2%
ValueCountFrequency (%)
17 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 10
 
0.2%
10 28
0.5%
9 11
 
0.2%
8 22
 
0.4%
7 44
0.7%
6 56
0.9%

CLNO
Real number (ℝ)

Missing  Zeros 

Distinct62
Distinct (%)1.1%
Missing222
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean21.296096
Minimum0
Maximum71
Zeros62
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:22.955772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median20
Q326
95-th percentile40
Maximum71
Range71
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.138933
Coefficient of variation (CV)0.47609351
Kurtosis1.1576727
Mean21.296096
Median Absolute Deviation (MAD)6
Skewness0.77505176
Sum122197
Variance102.79797
MonotonicityNot monotonic
2025-04-08T15:48:22.996552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 316
 
5.3%
19 307
 
5.2%
24 264
 
4.4%
23 259
 
4.3%
21 235
 
3.9%
20 231
 
3.9%
18 225
 
3.8%
25 221
 
3.7%
15 217
 
3.6%
22 212
 
3.6%
Other values (52) 3251
54.5%
(Missing) 222
 
3.7%
ValueCountFrequency (%)
0 62
1.0%
1 6
 
0.1%
2 15
 
0.3%
3 34
 
0.6%
4 42
 
0.7%
5 47
 
0.8%
6 60
1.0%
7 76
1.3%
8 92
1.5%
9 127
2.1%
ValueCountFrequency (%)
71 2
 
< 0.1%
65 3
 
0.1%
64 5
 
0.1%
63 1
 
< 0.1%
58 3
 
0.1%
57 1
 
< 0.1%
56 6
0.1%
55 14
0.2%
53 2
 
< 0.1%
52 5
 
0.1%

DEBTINC
Real number (ℝ)

Missing 

Distinct4693
Distinct (%)100.0%
Missing1267
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean33.779915
Minimum0.52449922
Maximum203.31215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2025-04-08T15:48:23.037107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.52449922
5-th percentile20.511886
Q129.140031
median34.818262
Q339.003141
95-th percentile42.767852
Maximum203.31215
Range202.78765
Interquartile range (IQR)9.8631093

Descriptive statistics

Standard deviation8.6017462
Coefficient of variation (CV)0.25464084
Kurtosis50.504042
Mean33.779915
Median Absolute Deviation (MAD)4.8149283
Skewness2.8523534
Sum158529.14
Variance73.990037
MonotonicityNot monotonic
2025-04-08T15:48:23.080074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.96414103 1
 
< 0.1%
41.57670052 1
 
< 0.1%
41.39546183 1
 
< 0.1%
20.68871482 1
 
< 0.1%
35.98208365 1
 
< 0.1%
37.82721876 1
 
< 0.1%
34.88554143 1
 
< 0.1%
41.82491442 1
 
< 0.1%
37.82317309 1
 
< 0.1%
42.17658506 1
 
< 0.1%
Other values (4683) 4683
78.6%
(Missing) 1267
 
21.3%
ValueCountFrequency (%)
0.5244992154 1
< 0.1%
0.7202950067 1
< 0.1%
0.8381175254 1
< 0.1%
1.028930968 1
< 0.1%
1.565931047 1
< 0.1%
1.603507978 1
< 0.1%
1.85553998 1
< 0.1%
1.909225163 1
< 0.1%
1.920694367 1
< 0.1%
2.365195413 1
< 0.1%
ValueCountFrequency (%)
203.3121487 1
< 0.1%
144.1890013 1
< 0.1%
143.949605 1
< 0.1%
133.5282704 1
< 0.1%
114.0505277 1
< 0.1%
91.61259998 1
< 0.1%
84.61388869 1
< 0.1%
84.37903408 1
< 0.1%
78.65438605 1
< 0.1%
76.42147805 1
< 0.1%

Interactions

2025-04-08T15:48:21.432595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.238253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.562944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.982141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.296000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.601109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.991918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.321960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.654696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.095816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.466541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.273525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.596651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.013377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.324734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.631212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.023671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.352600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.691062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.128360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.505500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.308075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.632905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.048285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.358027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.665184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.059803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.388042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.729363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.165785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.538472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.338293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.667717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.077160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.387525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.696307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.090961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.418971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.761995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.199681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.571350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.367155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.700838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.106749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.416057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.725150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.124859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.449679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.794005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.230650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.603749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.398540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.734972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.136598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.444953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.754811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.155890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.480462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.826271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.262931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.636468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.432077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.770638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.168382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.475754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.785867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.188962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.513487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.864858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.297936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.670536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.465575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.805952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.199150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.506501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.892543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.222079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.547718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.899660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.331565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.704310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.497727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.840859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.232005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.538608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.924781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.256478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.584488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.936350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.365392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.738856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.529931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:18.876201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.262948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.569279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:19.957967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.289731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.618718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:20.970461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-08T15:48:21.398501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-08T15:48:23.115474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BADCLAGECLNODEBTINCDELINQDEROGJOBLOANMORTDUENINQREASONVALUEYOJ
BAD1.0000.1640.1390.2960.3240.2530.1160.1210.0910.1820.0350.0960.089
CLAGE0.1641.0000.2420.008-0.000-0.0850.1050.1230.131-0.0980.1260.1930.173
CLNO0.1390.2421.0000.1920.1480.0410.1510.1430.3460.1490.2010.3640.045
DEBTINC0.2960.0080.1921.0000.0460.0140.0920.1200.1800.2050.0520.166-0.065
DELINQ0.324-0.0000.1480.0461.0000.2410.050-0.061-0.0270.0770.040-0.0360.029
DEROG0.253-0.0850.0410.0140.2411.0000.0360.007-0.0390.1720.000-0.054-0.067
JOB0.1160.1050.1510.0920.0500.0361.0000.1370.1740.0720.1460.1840.080
LOAN0.1210.1230.1430.120-0.0610.0070.1371.0000.1990.0430.3050.3460.092
MORTDUE0.0910.1310.3460.180-0.027-0.0390.1740.1991.0000.0560.1160.861-0.058
NINQ0.182-0.0980.1490.2050.0770.1720.0720.0430.0561.0000.1470.013-0.061
REASON0.0350.1260.2010.0520.0400.0000.1460.3050.1160.1471.0000.0660.103
VALUE0.0960.1930.3640.166-0.036-0.0540.1840.3460.8610.0130.0661.0000.032
YOJ0.0890.1730.045-0.0650.029-0.0670.0800.092-0.058-0.0610.1030.0321.000

Missing values

2025-04-08T15:48:21.828931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-08T15:48:21.870054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-08T15:48:21.934364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BADLOANMORTDUEVALUEREASONJOBYOJDEROGDELINQCLAGENINQCLNODEBTINC
01110025860.039025.0HomeImpOther10.50.00.094.3666671.09.0NaN
11130070053.068400.0HomeImpOther7.00.02.0121.8333330.014.0NaN
21150013500.016700.0HomeImpOther4.00.00.0149.4666671.010.0NaN
311500NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40170097800.0112000.0HomeImpOffice3.00.00.093.3333330.014.0NaN
51170030548.040320.0HomeImpOther9.00.00.0101.4660021.08.037.113614
61180048649.057037.0HomeImpOther5.03.02.077.1000001.017.0NaN
71180028502.043034.0HomeImpOther11.00.00.088.7660300.08.036.884894
81200032700.046740.0HomeImpOther3.00.02.0216.9333331.012.0NaN
912000NaN62250.0HomeImpSales16.00.00.0115.8000000.013.0NaN
BADLOANMORTDUEVALUEREASONJOBYOJDEROGDELINQCLAGENINQCLNODEBTINC
595008750055938.086794.0DebtConOther15.00.00.0223.8810400.016.036.753653
595108830054004.094838.0DebtConOther16.00.00.0193.7020510.015.036.262691
595208850050240.094687.0DebtConOther16.00.00.0214.4262060.016.034.751158
595308880053307.094058.0DebtConOther16.00.00.0218.3049780.015.034.242465
595408890048919.093371.0DebtConOther15.00.01.0205.6501590.015.034.818262
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